Oil refineries are under intense pressure to process lower quality crudes for reasons of price or availability. However, in many cases, oil refiners do not possess enough information and knowledge about certain crudes and how they behave in an operating environment to make processing these crudes feasible and optimal. Individual refiners only have access to information and knowledge about crudes they have actually used or tested.
In an effort to address the problem of not possessing enough information about certain crudes and how they behave in an operating environment, some refiners have used laboratory simulations to develop predictive models of certain performances. These models, however, are limited and do not address specific, often complex problems that may arise during processing of these crudes and how these problems can be alleviated by using appropriate chemical treatment solutions.
Linear programming systems have also been implemented which focus on defining crude cut and the corresponding cut yield, but these systems do not address the use of treatment chemicals in the crude selection mode. These methods cannot tell refiners how the crude blends will affect operations and equipment. Therefore, refiners lack important information necessary to access the economic viability of using these crudes.
Accordingly, there is a need for a method and system for assessing and optimizing crude selection which overcomes drawbacks in prior art methodologies and systems.